For our mobile robots Rhino and Minerva, which operated in the
Deutsches Museum Bonn and the US-Smithsonian's National
Museum of American History, the robustness and reliability of our
Markov localization system was of utmost importance. Accurate
position estimation was a crucial component, as many of the obstacles
were ``invisible'' to the robots' sensors (such as glass cages, metal
bars, staircases, and the alike). Given the estimate of the robot's
position [Fox et al.
1998b] integrated map information into the collision
avoidance system in order to prevent the robot from colliding with obstacles
that could not be detected.
Figure 12(a) shows a typical trajectory of the
robot Rhino, recorded in the museum in Bonn, along with the map used
for localization. The reader may notice that only the obstacles shown
in black were actually used for localization; the others were either
invisible or could not be detected reliably. Rhino used the entropy
filter to identify sensor readings that were corrupted by the presence
of people. Rhino's localization module was able to (1) globally
localize the robot in the morning when the robot was switched on and
(2) to reliably and accurately keep track of the robot's position. In
the entire six-day deployment period, in which Rhino traveled over
18km, our approach led only to a single software-related collision,
which involved an ``invisible'' obstacle and which was caused by a
localization error that was slightly larger than a 30cm safety margin.

Figure 12(b) shows a 2km long trajectory of the
robot Minerva in the National Museum of American History. Minerva
used the distance filter to identify readings reflected by unmodeled
objects. This filter was developed after Rhino's deployment in the
museum in Bonn, based on an analysis of the localization failure
reported above and in an attempt to prevent similar effects in future
installations. Based on the distance filter, Minerva was able to
operate reliably over a period of 13 days. During that time Minerva
traveled a total of 44km with a maximum speed of 1.63m/sec.

Unfortunately, the evidence from the museum projects is anecdotal.
Based on sensor data collected during Rhino's deployment in the museum
in Bonn, we also investigated the effect of our filter techniques more
systematically, and under even more extreme conditions. In particular,
we were interested in the localization results

when the environment is densely populated (more than 50%
of the sensor reading are corrupted), and

when the robot suffers extreme dead-reckoning errors
(e.g. induced by a person carrying the robot somewhere else). Since
such cases are rare, we manually inflicted such errors into the
original data to analyze their effect.